blueprint_for_prod_ai_based_code_generation.md

Untested but Promising Blueprint for Writing Production-Ready Code with AI Code Generation

What's Lacking / Required:

Defined processes, practices, and workflows.
The ability to generate working or easy to debug/fix code.
The ability to generate working or fixable production-ready code.
Efficient management of code files with thousands of lines of code (loc).
Management of file/code history, changes, and context.

Goals:

A simple workflow to build working/production front end code using AI-generated code. This can serve as a base or experiment upon which others can build solutions for other types of code.
Generated code must be, as much as possible, working or easy to debug requiring minimal changes to become functional.

This Approach Had Impressive Results in My Experiments (Scalability to a Full Project Needs Testing):

Passing pseudo-code or a skeleton/template to guide code generation.
Additionally, providing example code with the desired features and quality as context.
Generating decoupled code, ideally as a composition of multiple independent modules.
Debugging and fixing code at the file and module level. For now, without tests (since AI-generated tests would also need to be verified). Use tests if they are written or fixed by a human developer, and for production code.

Generate Production Code in Two Phases:

Phase 1: Base/Prototype

First, we generate a simple, working version of the required component. This version should be easy to understand, reason about, and already in a functional state. It will include the structure, layout, network calls, and state management that will either remain in the final version or serve as the foundation for it.

Phase 2: Production/Final

In the second phase, we use the working, simplified code to generate the final, production-ready version. This includes all enhancements, refinements, bug fixes, and changes necessary for production code.

Design/Requirements -> Base Code -> Production Code

The key is to effectively use pseudo code and skeletons, combined with high-quality context, to enforce quality and constraints at both stages. Maintaining two separate stages, a base stage and a production stage, is crucial.

Also follow the best practices as stated below.

I recently shared the best practices I discovered/found for coding using AI or code generation

https://gist.github.com/ditpoo/fe3d2955f59cee83ae8426fe2c24adcd

This is the experiment mentioned in the post AI to code a production project from scratch.

I'm thinking of coding a chat UI for llm's, there are so many of them so its easy to compare with for quality and output.

The one below is a 3k loc front end, entire code base can be added as context and is of good enough size & complexity to reason, understand and test with.

Fits nicely in the effective 32k context window of most llms/ai model right now.

Lastly why I'm sharing this and not building the next cursor or co-piolt myself, because its for big players and there is no real moat and no viability cost wise. But someone else can try their luck though.

ditpoo/blueprint_for_prod_ai_based_code_generation.md